{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "937ba2af-0665-44e3-b0c7-4941943132f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.nn as nn\n",
    "from mindspore import dtype as mstype\n",
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.vision.c_transforms as C\n",
    "import mindspore.dataset.transforms.c_transforms as C2\n",
    "from mindspore import context\n",
    "import matplotlib.pyplot as plt\n",
    "from mindspore.train.callback import Callback\n",
    "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor\n",
    "import os\n",
    "from mindspore import Model\n",
    "from mindspore.nn import Accuracy\n",
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits\n",
    "from VGG11 import VGG11\n",
    "from Resnet18 import ResNet18\n",
    "\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")\n",
    "\n",
    "\n",
    "def create_dataset(data_home, repeat_num=1, batch_size=32, do_train=True, device_target=\"GPU\"):\n",
    "    \"\"\"\n",
    "    create data for next use such as training or inferring\n",
    "    \"\"\"\n",
    "\n",
    "    cifar_ds = ds.Cifar10Dataset(data_home, num_parallel_workers=8, shuffle=True)\n",
    "\n",
    "    c_trans = []\n",
    "    if do_train:\n",
    "        c_trans += [\n",
    "            C.Resize((224, 224)),\n",
    "            C.RandomHorizontalFlip(prob=0.5)\n",
    "        ]\n",
    "\n",
    "    c_trans += [\n",
    "        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n",
    "        C.HWC2CHW()\n",
    "    ]\n",
    "\n",
    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
    "\n",
    "    cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n",
    "    cifar_ds = cifar_ds.map(operations=c_trans, input_columns=\"image\", num_parallel_workers=8)\n",
    "\n",
    "    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\n",
    "    cifar_ds = cifar_ds.repeat(repeat_num)\n",
    "\n",
    "    return cifar_ds\n",
    "\n",
    "\n",
    "class StepLossAccInfo(Callback):\n",
    "    def __init__(self, model, eval_dataset, steps_loss, steps_eval):\n",
    "        self.model = model\n",
    "        self.eval_dataset = eval_dataset\n",
    "        self.steps_loss = steps_loss\n",
    "        self.steps_eval = steps_eval\n",
    "\n",
    "    def step_end(self, run_context):\n",
    "        cb_params = run_context.original_args()\n",
    "        # cur_epoch = cb_params.cur_epoch_num\n",
    "        # cur_step = (cur_epoch-1)*1562 + cb_params.cur_step_num\n",
    "        cur_step = cb_params.cur_step_num\n",
    "        if cur_step % 10 == 0:\n",
    "            self.steps_loss[\"loss_value\"].append(str(cb_params.net_outputs))\n",
    "            self.steps_loss[\"step\"].append(str(cur_step))\n",
    "        if cur_step % 100 == 0:\n",
    "            acc = self.model.eval(self.eval_dataset, dataset_sink_mode=False)\n",
    "            self.steps_eval[\"step\"].append(cur_step)\n",
    "            self.steps_eval[\"acc\"].append(acc[\"Accuracy\"])\n",
    "\n",
    "\n",
    "def train(net_type, epoch_size):\n",
    "    # 设置网络结构\n",
    "    if net_type == \"vgg11\":\n",
    "        net = VGG11()\n",
    "    elif net_type == 'resnet18':\n",
    "        net = ResNet18()\n",
    "    # 设置损失函数和优化器\n",
    "    loss_fn = SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
    "    optimizer = nn.Adam(net.trainable_params(), learning_rate=1e-3)\n",
    "    # 初始化模型\n",
    "    model = Model(net, loss_fn=loss_fn, optimizer=optimizer, metrics={\"Accuracy\": Accuracy()})\n",
    "    # 设置训练数据集和测试数据集\n",
    "    ds_train_path = \"./datasets/cifar10/train/\"\n",
    "    ds_test_path = \"./datasets/cifar10/test/\"\n",
    "    ds_train = create_dataset(ds_train_path)\n",
    "    ds_test = create_dataset(ds_test_path)\n",
    "    # 设置模型保存的路径，删除掉之前训练保存的模型\n",
    "    model_path = \"./models/ckpt/mindspore_vision_application/\"\n",
    "    os.system('rm -f {0}*.ckpt {0}*.meta {0}*.pb'.format(model_path))\n",
    "    # 得到每个batch的训练步数\n",
    "    batch_num = ds_train.get_dataset_size()\n",
    "    # 设置与模型保存相关的参数\n",
    "    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=1)\n",
    "    ckpoint_cb = ModelCheckpoint(prefix=\"train_\" + net_type + \"_cifar10\", directory=model_path, config=config_ck)\n",
    "    # 设置损失记录器，参数是打印Loss信息的步长，142的意思是每142步打印一次loss信息\n",
    "    loss_cb = LossMonitor(142)\n",
    "    # 设置记录损失的数据结构，用于在后面打印曲线图像\n",
    "    steps_loss = {\"step\": [], \"loss_value\": []}\n",
    "    steps_eval = {\"step\": [], \"acc\": []}\n",
    "    step_loss_acc_info = StepLossAccInfo(model, ds_test, steps_loss, steps_eval)\n",
    "    # 执行模型的训练\n",
    "    print(\"begin training...\")\n",
    "    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, loss_cb, step_loss_acc_info], dataset_sink_mode=False)\n",
    "    # model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, loss_cb, step_loss_acc_info])\n",
    "    # 训练完后，对模型进行测试，打印正确率\n",
    "    res = model.eval(ds_test)\n",
    "    print(\"result: \", res)\n",
    "    # 利用训练中保存的信息，打印训练损失曲线和训练正确率曲线\n",
    "    steps = steps_loss[\"step\"]\n",
    "    loss_value = steps_loss[\"loss_value\"]\n",
    "    steps = list(map(int, steps))\n",
    "    loss_value = list(map(float, loss_value))\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(steps, loss_value, color=\"red\")\n",
    "    plt.xlabel(\"Steps\")\n",
    "    plt.ylabel(\"Loss_value\")\n",
    "    plt.title(\"Change chart of model loss value\")\n",
    "    steps = steps_eval[\"step\"]\n",
    "    acc_value = steps_eval[\"acc\"]\n",
    "    steps = list(map(int, steps))\n",
    "    loss_value = list(map(float, acc_value))\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(steps, acc_value, color=\"blue\")\n",
    "    plt.xlabel(\"Steps\")\n",
    "    plt.ylabel(\"Acc_value\")\n",
    "    plt.title(\"Change chart of model acc value\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d3a6ac8-6776-43aa-aa83-c7d643b865ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin training...\n",
      "epoch: 1 step: 142, loss is 1.9957772493362427\n",
      "epoch: 1 step: 284, loss is 1.9149317741394043\n",
      "epoch: 1 step: 426, loss is 1.7985364198684692\n",
      "epoch: 1 step: 568, loss is 1.670123815536499\n",
      "epoch: 1 step: 710, loss is 1.5040267705917358\n",
      "epoch: 1 step: 852, loss is 1.8950344324111938\n",
      "epoch: 1 step: 994, loss is 1.563089370727539\n",
      "epoch: 1 step: 1136, loss is 1.7251975536346436\n",
      "epoch: 1 step: 1278, loss is 1.5621157884597778\n",
      "epoch: 1 step: 1420, loss is 1.5709211826324463\n",
      "epoch: 1 step: 1562, loss is 1.5960967540740967\n",
      "result:  {'Accuracy': 0.46033653846153844}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(\"resnet18\", 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceaa79b5-1a58-4234-8f5a-f200c02875df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin training...\n",
      "epoch: 1 step: 142, loss is 2.1401305198669434\n",
      "epoch: 1 step: 284, loss is 2.0011603832244873\n",
      "epoch: 1 step: 426, loss is 2.1525518894195557\n",
      "epoch: 1 step: 568, loss is 1.7343541383743286\n",
      "epoch: 1 step: 710, loss is 1.7168153524398804\n",
      "epoch: 1 step: 852, loss is 1.6314562559127808\n",
      "epoch: 1 step: 994, loss is 1.5360771417617798\n",
      "epoch: 1 step: 1136, loss is 1.775369644165039\n",
      "epoch: 1 step: 1278, loss is 1.6938056945800781\n",
      "epoch: 1 step: 1420, loss is 1.5944005250930786\n",
      "epoch: 1 step: 1562, loss is 1.3563140630722046\n",
      "result:  {'Accuracy': 0.41576522435897434}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(\"vgg11\", 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf83743b-9043-4b8c-9b7d-cec0dbe86aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.nn as nn\n",
    "from mindspore import  context\n",
    "from mindspore import dtype as mstype\n",
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.transforms.c_transforms as C\n",
    "import mindspore.dataset.vision.c_transforms as VC\n",
    "from mindspore.train import Model\n",
    "from mindspore.train.callback import LossMonitor\n",
    "import numpy as np\n",
    "from mindspore.train.callback import Callback\n",
    "import matplotlib.pyplot as plt\n",
    "# 设置上下文\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")\n",
    "\n",
    "# 创建 CIFAR-10 数据集的加载函数\n",
    "def create_cifar10_lstm_dataset(data_path, batch_size=32,device_target=\"GPU\"):\n",
    "    # 加载 CIFAR-10 数据集\n",
    "    cifar_ds = ds.Cifar10Dataset(data_path)\n",
    "\n",
    "    # 图像转换操作\n",
    "    resize_op = VC.Resize((32, 32))\n",
    "    rescale_op = VC.Rescale(1.0 / 255.0, 0.0)\n",
    "    normalize_op = VC.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])\n",
    "    hwc2chw_op = VC.HWC2CHW()\n",
    "    type_cast_op = C.TypeCast(mstype.int32)\n",
    "    # 应用图像预处理操作\n",
    "    cifar_ds = cifar_ds.map(operations=[resize_op, rescale_op, normalize_op, hwc2chw_op], input_columns=\"image\")\n",
    "    cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns=\"label\")\n",
    "\n",
    "    # 批处理并打乱\n",
    "    cifar_ds = cifar_ds.shuffle(buffer_size=5000)\n",
    "    cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\n",
    "\n",
    "    return cifar_ds\n",
    "\n",
    "# 创建 LSTM 模型\n",
    "class LSTMClassifier(nn.Cell):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
    "        super(LSTMClassifier, self).__init__()\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers=num_layers, batch_first=True)\n",
    "        self.fc = nn.Dense(hidden_size, num_classes)\n",
    "\n",
    "    def construct(self, x):\n",
    "        # 将图像展平到序列形式\n",
    "        batch_size, channels, height, width = x.shape\n",
    "        flattened_input_size = channels * height * width\n",
    "\n",
    "        # 展平数据并将其转换为 LSTM 所期望的形状\n",
    "        x = x.view(batch_size, -1, flattened_input_size)\n",
    "        \n",
    "        # LSTM 前向传播\n",
    "        lstm_out, _ = self.lstm(x)\n",
    "\n",
    "        # 取最后一个时间步的输出进行分类\n",
    "        output = self.fc(lstm_out[:, -1, :])\n",
    "\n",
    "        return output\n",
    "\n",
    "# 创建训练数据集\n",
    "train_data_path = \"./datasets/cifar10/train/\"\n",
    "train_dataset = create_cifar10_lstm_dataset(train_data_path)\n",
    "\n",
    "'''\n",
    "任务四补全\n",
    "'''\n",
    "# 实例化 LSTM 模型\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "\n",
    "# 创建模型\n",
    "\n",
    "# 训练模型\n",
    "\n",
    "#结果可视化\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08547a9c-e7ef-40db-afa7-57d346e52ff6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MindSpore",
   "language": "python",
   "name": "mindspore"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
